R is a programming language and software specifically designed for statistical computing and graphics. It is widely used for quantitative data analysis, statistical modeling, and visualization.
Some notable features of R for quantitative data analytics and visualization include:
- Open source: R is an open-source software. Its source code is available for users to view, modify, and distribute freely.
- Statistical analysis: R provides a vast array of statistical techniques, including descriptive statistics, hypothesis testing, regression analysis, time-series analysis, clustering, and machine learning algorithms.
- Data visualization: R excels in data visualization with packages like ggplot2, lattice, and others. These packages allow users to create high-quality plots, charts, and graphs visually representing complex data patterns and trends.
- Data manipulation and transformation: Users can reshape datasets, merge and aggregate data, and perform a variety of data wrangling tasks using packages such as dplyr and tidyr.
- Programming flexibility: R is a programming language with a syntax that allows users to write scripts and functions for customized analyses. This makes it a powerful tool for those who want more control and flexibility in their data analysis workflows.
- Integration with other tools: R can be integrated with other software tools and databases, allowing for a seamless workflow. It is often used with tools like RStudio, a popular integrated development environment (IDE) for R, to enhance the coding and visualization experience.
List of recommended resources #
For a broad overview #
This paper, by C. Rajeswari, Dyuti Basu and Namita Maurya, does a comparative analysis of the two popular data analytics tools: Tableau and R.
This research article by Emily Nordmann, Phil McAleer and Lisa M. DeBruine provides a practical introduction to data visualization using R specifically aimed at researchers who have little to no prior experience of using R.
This database contains a collection of charts made using the R programming language.
For in depth understanding #
This book by Thomas Rahlf gives a comprehensive introduction to creating presentation graphics with R. It includes step-by-step explanations of the programming of figures based on real data as well as the complete code of 100 examples from different fields.
This book by Hadley Wickham, Mine Cetinkaya-Rundel & Garrett Grolemund gives a practicum of skills for data science. It teaches the grammar of graphics, literate programming, and reproducible research to save time.
Case study #
Case Study: New York Taxi Cabs – This case study uses ggplot2 library of R programming language to plot data containing information on every single trip taken with a yellow New York City taxi cab in the month of June, 2015.
This article by Paul Brennan uses the programming language R for reproducible data visualizations.